Publications

@inproceedings{li_exploring_2025,

title = {Exploring Large Language Model-Driven Agents for Environment-Aware Spatial Interactions and Conversations in Virtual Reality Role-Play Scenarios},

author = {Z. Li and H. Zhang and C. Peng and R. Peiris},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002706893&doi=10.1109%2fVR59515.2025.00025&partnerID=40&md5=60f22109e054c9035a0c2210bb797039},

doi = {10.1109/VR59515.2025.00025},

isbn = {979-833153645-9 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Proc. - IEEE Conf. Virtual Real. 3D User Interfaces, VR},

pages = {1–11},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Recent research has begun adopting Large Language Model (LLM) agents to enhance Virtual Reality (VR) interactions, creating immersive chatbot experiences. However, while current studies focus on generating dialogue from user speech inputs, their abilities to generate richer experiences based on the perception of LLM agents' VR environments and interaction cues remain unexplored. Hence, in this work, we propose an approach that enables LLM agents to perceive virtual environments and generate environment-aware interactions and conversations for an embodied human-AI interaction experience in VR environments. Here, we define a schema for describing VR environments and their interactions through text prompts. We evaluate the performance of our method through five role-play scenarios created using our approach in a study with 14 participants. The findings discuss the opportunities and challenges of our proposed approach for developing environment-aware LLM agents that facilitate spatial interactions and conversations within VR role-play scenarios. © 2025 IEEE.},

keywords = {Chatbots, Computer simulation languages, Context- awareness, context-awareness, Digital elevation model, Generative AI, Human-AI Interaction, Language Model, Large language model, large language models, Model agents, Role-play simulation, role-play simulations, Role-plays, Spatial interaction, Virtual environments, Virtual Reality, Virtual-reality environment},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lau_evaluating_2025,

title = {Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling},

author = {K. H. C. Lau and E. Bozkir and H. Gao and E. Kasneci},

editor = {Del Bue A. and Canton C. and Pont-Tuset J. and Tommasi T.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105006905979&doi=10.1007%2f978-3-031-91572-7_11&partnerID=40&md5=8a81fb09ff54e57b9429660a8898149a},

doi = {10.1007/978-3-031-91572-7_11},

isbn = {03029743 (ISSN); 978-303191571-0 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15628 LNCS},

pages = {177–195},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This paper explores the innovative application of Large Language Models (LLMs) in Virtual Reality (VR) environments to promote heritage education, focusing on traditional Scottish curling presented in the game “Scottish Bonspiel VR”. Our study compares the effectiveness of LLM-based chatbots with pre-defined scripted chatbots, evaluating key criteria such as usability, user engagement, and learning outcomes. The results show that LLM-based chatbots significantly improve interactivity and engagement, creating a more dynamic and immersive learning environment. This integration helps document and preserve cultural heritage and enhances dissemination processes, which are crucial for safeguarding intangible cultural heritage (ICH) amid environmental changes. Furthermore, the study highlights the potential of novel technologies in education to provide immersive experiences that foster a deeper appreciation of cultural heritage. These findings support the wider application of LLMs and VR in cultural education to address global challenges and promote sustainable practices to preserve and enhance cultural heritage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {Chatbots, Cultural heritages, Digital Cultural Heritage, Digital cultural heritages, Educational robots, Engineering education, Heritage education, Historic Preservation, Language Model, Large language model, large language models, Learning outcome, Model-based OPC, Usability engineering, User Engagement, Virtual Reality, Virtual-reality environment, Virtualization},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{carcangiu_conversational_2025,

title = {Conversational Rule Creation in XR: User’s Strategies in VR and AR Automation},

author = {A. Carcangiu and M. Manca and J. Mereu and C. Santoro and L. Simeoli and L. D. Spano},

editor = {Santoro C. and Schmidt A. and Matera M. and Bellucci A.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105009012634&doi=10.1007%2f978-3-031-95452-8_4&partnerID=40&md5=67e2b8ca4bb2b508cd41548e3471705b},

doi = {10.1007/978-3-031-95452-8_4},

isbn = {03029743 (ISSN); 978-303195451-1 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15713 LNCS},

pages = {59–79},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {Rule-based approaches allow users to customize XR environments. However, the current menu-based interfaces still create barriers for end-user developers. Chatbots based on Large Language Models (LLMs) have the potential to reduce the threshold needed for rule creation, but how users articulate their intentions through conversation remains under-explored. This work investigates how users express event-condition-action automation rules in Virtual Reality (VR) and Augmented Reality (AR) environments. Through two user studies, we show that the dialogues share consistent strategies across the interaction setting (keywords, difficulties in expressing conditions, task success), even if we registered different adaptations for each setting (verbal structure, event vs action first rules). Our findings are relevant for the design and implementation of chatbot-based support for expressing automations in an XR setting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.},

keywords = {'current, Automation, Chatbots, Condition, End-User Development, Extended reality, Human computer interaction, Immersive authoring, Language Model, Large language model, large language models, Rule, Rule-based approach, rules, User interfaces},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{zhou_real-time_2025,

title = {Real-Time Full-body Interaction with AI Dance Models: Responsiveness to Contemporary Dance},

author = {J. Zhou and R. Weber and E. Wen and D. Lottridge},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105001922427&doi=10.1145%2f3708359.3712077&partnerID=40&md5=cea9213198220480b80b7a4840d26ccc},

doi = {10.1145/3708359.3712077},

isbn = {979-840071306-4 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Int Conf Intell User Interfaces Proc IUI},

pages = {1177–1187},

publisher = {Association for Computing Machinery},

abstract = {Interactive AI chatbots put the power of Large-Language Models (LLMs) into people's hands; it is this interactivity that fueled explosive worldwide influence. In the generative dance space, however, there are few deep-learning-based generative dance models built with interactivity in mind. The release of the AIST++ dance dataset in 2021 led to an uptick of capabilities in generative dance models. Whether these models could be adapted to support interactivity and how well this approach will work is not known. In this study, we explore the capabilities of existing generative dance models for motion-to-motion synthesis on real-time, full-body motion-captured contemporary dance data. We identify an existing model that we adapted to support interactivity: the Bailando++ model, which is trained on the AIST++ dataset and was modified to take music and a motion sequence as input parameters in an interactive loop. We worked with two professional contemporary choreographers and dancers to record and curate a diverse set of 203 motion-captured dance sequences as a set of "user inputs"captured through the Optitrack high-precision motion capture 3D tracking system. We extracted 17 quantitative movement features from the motion data using the well-established Laban Movement Analysis theory, which allowed for quantitative comparisons of inter-movement correlations, which we used for clustering input data and comparing input and output sequences. A total of 10 pieces of music were used to generate a variety of outputs using the adapted Bailando++ model. We found that, on average, the generated output motion achieved only moderate correlations to the user input, with some exceptions of movement and music pairs achieving high correlation. The high-correlation generated output sequences were deemed responsive and relevant co-creations in relation to the input sequences. We discuss implications for interactive generative dance agents, where the use of 3D joint coordinate data should be used over SMPL parameters for ease of real-time generation, and how the use of Laban Movement Analysis could be used to extract useful features and fine-tune deep-learning models. © 2025 Copyright held by the owner/author(s).},

keywords = {3D modeling, Chatbots, Computer interaction, Deep learning, Deep-Learning Dance Model, Design of Human-Computer Interaction, Digital elevation model, Generative AI, Input output programs, Input sequence, Interactivity, Motion capture, Motion tracking, Movement analysis, Output sequences, Problem oriented languages, Real- time, Text mining, Three dimensional computer graphics, User input, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gaglio_intention_2025,

title = {Intention Reading Architecture for Virtual Agents},

author = {G. F. Gaglio and S. Vinanzi and A. Cangelosi and A. Chella},

editor = {Palinko O. and Bodenhagen L. and Cabibihan J.-J. and Fischer K. and Šabanović S. and Winkle K. and Behera L. and Ge S.S. and Chrysostomou D. and Jiang W. and He H.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105002042645&doi=10.1007%2f978-981-96-3522-1_41&partnerID=40&md5=70ccc7039785bb4ca4d45752f1d3587f},

doi = {10.1007/978-981-96-3522-1_41},

isbn = {03029743 (ISSN); 978-981963521-4 (ISBN)},

year  = {2025},

date = {2025-01-01},

booktitle = {Lect. Notes Comput. Sci.},

volume = {15561 LNAI},

pages = {488–497},

publisher = {Springer Science and Business Media Deutschland GmbH},

abstract = {This work presents the development of a virtual agent designed specifically for use in the Metaverse, video games, and other virtual environments, capable of performing intention reading on a human-controlled avatar through a cognitive architecture that endows it with contextual awareness. The paper explores the adaptation of a cognitive architecture, originally developed for physical robots, to a fully virtual context, where it is integrated with a Large Language Model to create highly communicative virtual assistants. Although this work primarily focuses on virtual applications, integrating cognitive architectures with LLMs marks a significant step toward creating collaborative artificial agents capable of providing meaningful support by deeply understanding context and user intentions in digital environments. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.},

keywords = {Chatbots, Cognitive Architecture, Cognitive Architectures, Computer simulation languages, Intelligent virtual agents, Intention Reading, Intention readings, Language Model, Large language model, Metaverse, Metaverses, Physical robots, Video-games, Virtual agent, Virtual assistants, Virtual contexts, Virtual environments, Virtual machine},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{lee_utilizing_2024,

title = {Utilizing Virtual Reality and Generative AI Chatbot for Job Interview Simulations},

author = {L. -K. Lee and E. H. Chan and K. K. -L. Tong and N. K. -H. Wong and B. S. -Y. Wu and Y. -C. Fung and E. K. S. Fong and U. Leong Hou and N. -I. Wu},

editor = {Chui K.T. and Hui Y.K. and Yang D. and Lee L.-K. and Wong L.-P. and Reynolds B.L.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206582338&doi=10.1109%2fISET61814.2024.00048&partnerID=40&md5=c6986c0697792254e167e143b75f14c6},

doi = {10.1109/ISET61814.2024.00048},

isbn = {979-835036141-4 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - Int. Symp. Educ. Technol., ISET},

pages = {209–212},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Stress and anxiety experienced by interviewees, particularly fresh graduates, would significantly impact their performance in job interviews. Due to the increased affordability and user-friendliness of virtual reality (VR), VR has seen a surge in its application within the educational sector. This paper presents the design and implementation of a job interview simulation system, leveraging VR and a generative AI chatbot to provide an immersive environment for computer science graduates in Hong Kong. The system aims to help graduates practice and familiarize themselves with various real-world scenarios of a job interview in English, Mandarin, and Cantonese, tailored to the unique language requirements of Hong Kong's professional environment. The system comprises three core modules: a mock question and answer reading module, an AI speech analysis module, and a virtual interview module facilitated by the generative AI chatbot, ChatGPT. We anticipate that the proposed simulator will provide valuable insights to education practitioners on utilizing VR and generative AI for job interview training, extending beyond computer science graduates. © 2024 IEEE.},

keywords = {chatbot, Chatbots, Computer interaction, Computer simulation languages, Generative adversarial networks, Generative AI, Hong-kong, Human computer interaction, ITS applications, Job interview simulation, Job interviews, Performance, Science graduates, User friendliness, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{gao_datalivr_2024,

title = {DataliVR: Transformation of Data Literacy Education through Virtual Reality with ChatGPT-Powered Enhancements},

author = {H. Gao and H. Huai and S. Yildiz-Degirmenci and M. Bannert and E. Kasneci},

editor = {Eck U. and Sra M. and Stefanucci J. and Sugimoto M. and Tatzgern M. and Williams I.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85213525613&doi=10.1109%2fISMAR62088.2024.00026&partnerID=40&md5=abdeba7ecfecc8b1d715d633a29bd11d},

doi = {10.1109/ISMAR62088.2024.00026},

isbn = {979-833151647-5 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Symp. Mixed Augment. Real., ISMAR},

pages = {120–129},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital transformation, innovative technologies such as Virtual Reality (VR) offer promising avenues for immersive and engaging learning experiences. This paper introduces DataliVR, a pioneering VR application aimed at enhancing the data literacy skills of university students within a contextual and gamified virtual learning environment. By integrating Large Language Models (LLMs) like ChatGPT as a conversational artificial intelligence (AI) chatbot embodied within a virtual avatar, DataliVR provides personalized learning assistance, enriching user learning experiences. Our study employed an experimental approach, with chatbot availability as the independent variable, analyzing learning experiences and outcomes as dependent variables with a sample of thirty participants. Our approach underscores the effectiveness and user-friendliness of ChatGPT-powered DataliVR in fostering data literacy skills. Moreover, our study examines the impact of the ChatGPT-based AI chatbot on users' learning, revealing significant effects on both learning experiences and outcomes. Our study presents a robust tool for fostering data literacy skills, contributing significantly to the digital advancement of data literacy education through cutting-edge VR and AI technologies. Moreover, our research provides valuable insights and implications for future research endeavors aiming to integrate LLMs (e.g., ChatGPT) into educational VR platforms. © 2024 IEEE.},

keywords = {Adversarial machine learning, Chatbots, ChatGPT, Contrastive Learning, Data driven, Data literacy, Digital transformation, Federated learning, Immersive learning, Language Model, Large language model, Learning experiences, Learning outcome, LLMs, Virtual environments, Virtual Reality},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jia_nivta_2024,

title = {NivTA: Towards a Naturally Interactable Edu-Metaverse Teaching Assistant for CAVE},

author = {Y. Jia and Z. P. T. Sin and X. E. Wang and C. Li and P. H. F. Ng and X. Huang and J. Dong and Y. Wang and G. Baciu and J. Cao and Q. Li},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211447638&doi=10.1109%2fMetaCom62920.2024.00023&partnerID=40&md5=efefd453c426e74705518254bdc49e87},

doi = {10.1109/MetaCom62920.2024.00023},

isbn = {979-833151599-7 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Proc. - IEEE Int. Conf. Metaverse Comput., Netw., Appl., MetaCom},

pages = {57–64},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {Edu-metaverse is a specialized metaverse dedicated for interactive education in an immersive environment. Its main purpose is to immerse the learners in a digital environment and conduct learning activities that could mirror reality. Not only does it enable activities that may be difficult to perform in the real world, but it also extends the interaction to personalized and CL. This is a more effective pedagogical approach as it tends to enhance the motivation and engagement of students and it increases their active participation in lessons delivered. To this extend, we propose to realize an interactive virtual teaching assistant called NivTA. To make NivTA easily accessible and engaging by multiple users simultaneously, we also propose to use a CAVE virtual environment (CAVE-VR) as a "metaverse window"into concepts, ideas, topics, and learning activities. The students simply need to step into the CAVE-VR and interact with a life-size teaching assistant that they can engage with naturally, as if they are approaching a real person. Instead of text-based interaction currently developed for large language models (LLM), NivTA is given additional cues regarding the users so it can react more naturally via a specific prompt design. For example, the user can simply point to an educational concept and ask NivTA to explain what it is. To guide NivTA onto the educational concept, the prompt is also designed to feed in an educational KG to provide NivTA with the context of the student's question. The NivTA system is an integration of several components that are discussed in this paper. We further describe how the system is designed and implemented, along with potential applications and future work on interactive collaborative edu-metaverse environments dedicated for teaching and learning. © 2024 IEEE.},

keywords = {Active learning, Adversarial machine learning, cave automatic virtual environment, Cave automatic virtual environments, Caves, Chatbots, Contrastive Learning, Digital elevation model, Federated learning, Interactive education, Language Model, Large language model agent, Learning Activity, LLM agents, Metaverses, Model agents, Natural user interface, Students, Teaching, Teaching assistants, Virtual environments, Virtual Reality, virtual teaching assistant, Virtual teaching assistants},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{prasanna_kumaar_wristview_2024,

title = {WRISTVIEW: Augmented Reality and Generative AI Integration for Enhanced Online Shopping Experiences},

author = {D. Prasanna Kumaar and D. Abisha and J. Ida Christy and R. Navedha Evanjalin and P. Rajesh and K. Mohamed Haris},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208587489&doi=10.1109%2fI-SMAC61858.2024.10714789&partnerID=40&md5=e7742c8808cb551b17efe9ac3efeb961},

doi = {10.1109/I-SMAC61858.2024.10714789},

isbn = {979-835037642-5 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {Int. Conf. I-SMAC (IoT Soc., Mob., Anal. Cloud), I-SMAC - Proc.},

pages = {1115–1120},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

abstract = {The traditional retail experience for purchasing watches lacks the interactivity and personalization that modern consumers seek. With the increasing shift towards online shopping platforms, there is a growing demand for an engaging and immersive virtual experience that allows customers to explore and try on watches. The absence of expert guidance during the online shopping process often results in less informed decision-making. To address these challenges, WRISTVIEW presents an innovative solution that integrates augmented reality (AR) and a conversational Generative AI (GENAI) chatbot. The AR component enables users to virtually try on watches, providing a realistic and interactive experience. The GENAI chatbot enhances this experience by offering expert advice, answering queries, and guiding users through the watch shopping journey, thereby creating a more personalized and informative shopping process. The objective of this research is to bridge the gap between the traditional in-store watch shopping experience and the online environment, ensuring that customers can make well-informed and satisfying purchase decisions in a virtual setting. The development, implementation, and potential impact of combining AR and GENAI technologies to transform the online watch shopping experience are discussed. © 2024 IEEE.},

keywords = {Augmented Reality, Chatbots, Conversational AI, Customer satisfaction, Decision making, E-commerce Innovation, Growing demand, Immersive, Interactivity, Online shopping, Personalizations, Personalized Shopping Experience, Purchasing, Sales, Virtual environments, Virtual Try-On},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jiang_situ_2024,

title = {In Situ 3D Scene Synthesis for Ubiquitous Embodied Interfaces},

author = {H. Jiang and L. Song and D. Weng and Z. Sun and H. Li and X. Dongye and Z. Zhang},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85209812307&doi=10.1145%2f3664647.3681616&partnerID=40&md5=e58acd404c8785868c69a4647cecacb2},

doi = {10.1145/3664647.3681616},

isbn = {979-840070686-8 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {MM - Proc. ACM Int. Conf. Multimed.},

pages = {3666–3675},

publisher = {Association for Computing Machinery, Inc},

abstract = {Virtual reality enables us to access and interact with immersive virtual environments anytime and anywhere in various fields such as entertainment, training, and education. However, users immersed in virtual scenes remain physically connected to their real-world surroundings, which can pose safety and immersion challenges. Although virtual scene synthesis has attracted widespread attention, many popular methods are limited to generating purely virtual scenes independent of physical environments or simply mapping physical objects as obstacles. To this end, we propose a scene agent that synthesizes situated 3D virtual scenes as a kind of ubiquitous embodied interface in VR for users. The scene agent synthesizes scenes by perceiving the user's physical environment as well as inferring the user's demands. The synthesized scenes maintain the affordances of the physical environment, enabling immersive users to interact with the physical environment and improving the user's sense of security. Meanwhile, the synthesized scenes maintain the style described by the user, improving the user's immersion. The comparison results show that the proposed scene agent can synthesize virtual scenes with better affordance maintenance, scene diversity, style maintenance, and 3D intersection over union compared to baselines. To the best of our knowledge, this is the first work that achieves in situ scene synthesis with virtual-real affordance consistency and user demand. © 2024 ACM.},

keywords = {3D modeling, 3D scenes, affordance, Affordances, Chatbots, Computer simulation languages, Digital elevation model, Embodied interfaces, Language Model, Large language model, Physical environments, Scene synthesis, Synthesised, Three dimensional computer graphics, user demand, User demands, Virtual environments, Virtual Reality, Virtual scenes},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augelloIntroducingNarRobRobotic2019,

title = {Introducing NarRob, a Robotic Storyteller},

author = { Agnese Augello and Ignazio Infantino and Umberto Maniscalco and Giovanni Pilato and Filippo Vella},

editor = { M Gentile and M Allegra and H Sobke},

doi = {10.1007/978-3-030-11548-7_36},

isbn = {978-3-030-11548-7 978-3-030-11547-0},

year  = {2019},

date = {2019-01-01},

booktitle = {GAMES AND LEARNING ALLIANCE, GALA 2018},

volume = {11385},

pages = {387--396},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Lecture Notes in Computer Science},

abstract = {In this work we introduce NarRob, a robot able to engage in conversations and tell stories, by accompanying the speech with proper gestures. We discuss about the main components of the robot's architecture, and some possible education experiments that we are planning to carry out in real scholastic contexts.},

keywords = {Bullying, Chatbots, Conversational Agents, Education, Robotics, Social Practices, Storytelling Robots},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augello_introducing_2019,

title = {Introducing NarRob, a Robotic Storyteller},

author = {Agnese Augello and Ignazio Infantino and Umberto Maniscalco and Giovanni Pilato and Filippo Vella},

editor = {M Gentile and M Allegra and H Sobke},

doi = {10.1007/978-3-030-11548-7_36},

isbn = {978-3-030-11548-7 978-3-030-11547-0},

year  = {2019},

date = {2019-01-01},

booktitle = {GAMES AND LEARNING ALLIANCE, GALA 2018},

volume = {11385},

pages = {387–396},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Lecture Notes in Computer Science},

abstract = {In this work we introduce NarRob, a robot able to engage in conversations and tell stories, by accompanying the speech with proper gestures. We discuss about the main components of the robot's architecture, and some possible education experiments that we are planning to carry out in real scholastic contexts.},

keywords = {Bullying, Chatbots, Conversational Agents, Education, Robotics, Social Practices, Storytelling Robots},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augelloOverviewOpenSourceChatbots2018,

title = {An Overview of Open-Source Chatbots Social Skills},

author = { Agnese Augello and Manuel Gentile and Frank Dignum},

editor = { S Diplaris and A Satsiou and A Folstad and M Vafopoulos and T Vilarinho},

doi = {10.1007/978-3-319-77547-0_18},

isbn = {978-3-319-77547-0 978-3-319-77546-3},

year  = {2018},

date = {2018-01-01},

booktitle = {INTERNET SCIENCE, INSCI 2017},

volume = {10750},

pages = {236--248},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Lecture Notes in Computer Science},

abstract = {This paper aims to analyze and compare some of the most known open source chatbot technologies focusing on their potential to model a conversational agent able to show a form of ``social intelligence''. The main features and drawbacks of each system will be examined. Then, we will discuss their flexibility to produce more realistic social conversational scenarios adopting as the reference the social practice theory.},

keywords = {Chatbots, Conversational Agents, Social Practices},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{messinaBioGraphBotConversationalAssistant2018,

title = {BioGraphBot: A Conversational Assistant for Bioinformatics Graph Databases},

author = { Antonio Messina and Agnese Augello and Giovanni Pilato and Riccardo Rizzo},

editor = { L Barolli and T Enokido},

doi = {10.1007/978-3-319-61542-4_12},

isbn = {978-3-319-61542-4 978-3-319-61541-7},

year  = {2018},

date = {2018-01-01},

booktitle = {INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING, IMIS-2017},

volume = {612},

pages = {135--146},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Advances in Intelligent Systems and Computing},

abstract = {Chatbots technology allows to easily add a conversational interface to a large set of applications. In this paper, we show a conversational agent based on ALICE framework aimed at playing the role of interface between human users and a bioinformatics graph database. The system has been embedded into the web frontend of BioGraphDB, a publicly available resource that uses Gremlin as the primary query language. To be successfully exploited, domain experts, such as biologists and bioinformaticians, should also have familiarity with that query language. The use of a chatbot allows translating queries expressed in natural language to queries expressed in Gremlin, simplifying the interaction with BioGraphDB.},

keywords = {Biomedical, Chatbots, Gremlin, Human computer interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augello_overview_2018,

title = {An Overview of Open-Source Chatbots Social Skills},

author = {Agnese Augello and Manuel Gentile and Frank Dignum},

editor = {S Diplaris and A Satsiou and A Folstad and M Vafopoulos and T Vilarinho},

doi = {10.1007/978-3-319-77547-0_18},

isbn = {978-3-319-77547-0 978-3-319-77546-3},

year  = {2018},

date = {2018-01-01},

booktitle = {INTERNET SCIENCE, INSCI 2017},

volume = {10750},

pages = {236–248},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Lecture Notes in Computer Science},

abstract = {This paper aims to analyze and compare some of the most known open source chatbot technologies focusing on their potential to model a conversational agent able to show a form of “social intelligence”. The main features and drawbacks of each system will be examined. Then, we will discuss their flexibility to produce more realistic social conversational scenarios adopting as the reference the social practice theory.},

keywords = {Chatbots, Conversational Agents, Social Practices},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{messina_biographbot_2018,

title = {BioGraphBot: A Conversational Assistant for Bioinformatics Graph Databases},

author = {Antonio Messina and Agnese Augello and Giovanni Pilato and Riccardo Rizzo},

editor = {L Barolli and T Enokido},

doi = {10.1007/978-3-319-61542-4_12},

isbn = {978-3-319-61542-4 978-3-319-61541-7},

year  = {2018},

date = {2018-01-01},

booktitle = {INNOVATIVE MOBILE AND INTERNET SERVICES IN UBIQUITOUS COMPUTING, IMIS-2017},

volume = {612},

pages = {135–146},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Advances in Intelligent Systems and Computing},

abstract = {Chatbots technology allows to easily add a conversational interface to a large set of applications. In this paper, we show a conversational agent based on ALICE framework aimed at playing the role of interface between human users and a bioinformatics graph database. The system has been embedded into the web frontend of BioGraphDB, a publicly available resource that uses Gremlin as the primary query language. To be successfully exploited, domain experts, such as biologists and bioinformaticians, should also have familiarity with that query language. The use of a chatbot allows translating queries expressed in natural language to queries expressed in Gremlin, simplifying the interaction with BioGraphDB.},

keywords = {Biomedical, Chatbots, Gremlin, Human computer interaction},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augelloModelSocialChatbot2016,

title = {A Model of a Social Chatbot},

author = { Agnese Augello and Manuel Gentile and Lucas Weideveld and Frank Dignum},

editor = { G DePietro and Luigi Gallo and Robert J. Howlett and Lakhmi C. Jain},

doi = {10.1007/978-3-319-39345-2_57},

isbn = {978-3-319-39345-2 978-3-319-39344-5},

year  = {2016},

date = {2016-01-01},

booktitle = {INTELLIGENT INTERACTIVE MULTIMEDIA SYSTEMS AND SERVICES 2016},

volume = {55},

pages = {637--647},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Smart Innovation Systems and Technologies},

abstract = {Traditional chatbots lack the capability to correctly manage conversations according to the social context. However a dialogue is a joint activity that must consider both individual and social processes. In this work we propose a model of a social chatbot able to choose the most suitable dialogue plans according to what in sociological literature is called a ``social practice''. The proposed model is discussed considering a case study of a work in progress aimed at the development of a serious game for communicative skills learning.},

keywords = {Chatbots, Serious game, Social Practices},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{augello_model_2016,

title = {A Model of a Social Chatbot},

author = {Agnese Augello and Manuel Gentile and Lucas Weideveld and Frank Dignum},

editor = {G DePietro and Luigi Gallo and Robert J. Howlett and Lakhmi C. Jain},

doi = {10.1007/978-3-319-39345-2_57},

isbn = {978-3-319-39345-2 978-3-319-39344-5},

year  = {2016},

date = {2016-01-01},

booktitle = {IN℡LIGENT INTERACTIVE MULTIMEDIA SYSTEMS AND SERVICES 2016},

volume = {55},

pages = {637–647},

publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},

address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},

series = {Smart Innovation Systems and Technologies},

abstract = {Traditional chatbots lack the capability to correctly manage conversations according to the social context. However a dialogue is a joint activity that must consider both individual and social processes. In this work we propose a model of a social chatbot able to choose the most suitable dialogue plans according to what in sociological literature is called a “social practice”. The proposed model is discussed considering a case study of a work in progress aimed at the development of a serious game for communicative skills learning.},

keywords = {Chatbots, Serious game, Social Practices},

pubstate = {published},

tppubtype = {inproceedings}

}